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Periodic frequent patterns are sets of events or items that periodically appear in a sequence of events or transactions. Many algorithms have been designed to identify periodic frequent patterns in data. However, most assume that the periodic behavior of a pattern does not change much over time. To address this limitation, this paper proposes to discover a novel type of periodic patterns in a sequence of events or transactions, called Local Periodic Patterns (LPPs) which are patterns (sets of events) that have a periodic behavior in some non predefined time-intervals. A pattern is said to be a local periodic pattern if it appears regularly and continuously in some time-interval(s). Two novel measures are proposed to assess the periodicity and frequency of patterns in time-intervals. The maxSoPer (maximal period of spillovers) measure allows detecting time-intervals of variable lengths where a pattern is continuously periodic, while the minDur (minimal duration) measure ensures that those time-intervals have a minimum duration. To discover all LPPs, the paper presents three efficient algorithms. An experimental evaluation on real datasets shows that the proposed algorithms are efficient and can provide useful patterns that cannot be found using traditional periodic pattern mining algorithms. [ABSTRACT FROM AUTHOR]

Utility is an important concept in Economics. A variety of applications consider utility in real-life situations, which has lead to the emergence of utility-oriented mining (also called utility mining) in the recent decade. Utility mining has attracted a great amount of attention, but most of the existing studies have been developed to deal with itemset-based data. Time-ordered sequence data is more commonly seen in real-world situations, which is different from itemset-based data. Since they are time-consuming and require large amount of memory usage, current utility mining algorithms still have limitations when dealing with sequence data. In addition, the mining efficiency of utility mining on sequence data still needs to be improved, especially for long sequences or when there is a low minimum utility threshold. In this paper, we propose an efficient Pro jection-based U tility M ining (ProUM) approach to discover high-utility sequential patterns from sequence data. The utility-array structure is designed to store the necessary information of the sequence-order and utility. ProUM can significantly improve the mining efficiency by utilizing the projection technique in generating utility-array, and it effectively reduces the memory consumption. Furthermore, a new upper bound named sequence extension utility is proposed and several pruning strategies are further applied to improve the efficiency of ProUM. By taking utility theory into account, the derived high-utility sequential patterns have more insightful and interesting information than other kinds of patterns. Experimental results showed that the proposed ProUM algorithm significantly outperformed the state-of-the-art algorithms in terms of execution time, memory usage, and scalability. [ABSTRACT FROM AUTHOR]

• A general and flexible type of sequence pattern called tri-pattern is introduced. • The relationships among the new type and four existing types are analyzed. • An efficient Apriori algorithm for frequent tri-patterns discovery is designed. • Comparison on three application areas shows the superiority of tri-patterns. The concept of patterns is the basis of sequence analysis. There are various pattern definitions for biological data, texts, and time series. Inspired by the methodology of three-way decisions and protein tri-partition, this paper proposes a frequent pattern discovery algorithm for a new type of pattern by dividing the alphabet into strong, medium, and weak parts. The new type, called a tri-pattern, is more general and flexible than existing ones and is therefore more interesting in applications. Experiments were undertaken on data in various fields to reveal the universality of this new pattern. These include protein sequence mining, petroleum production time series analysis, and forged Chinese text keyword mining. The results show that tri-patterns are more meaningful and desirable than the existing four types of patterns. This study enriches the semantics of sequential pattern discovery and the application fields of three-way decisions. [ABSTRACT FROM AUTHOR]

Location-based Social Networks (LBSN) such as Foursquare have become ubiquitous due to the vast spread of smart mobile devices. The users of LBSN can check in at any locations, write tips, share comments, etc. LBSN has attracted much research attention due to the fact that the large volume of check-ins provides an unprecedented channel to connect users’ online world to their offline physical activities. An essential analysis task is to identify hidden community from these check-ins, i.e., the community not explicitly defined based on the online links in LBSN but implicitly exist in the mobility patterns from these check-ins. The current state-of-the-art systems explore user check-ins to extract the visited venues; analyze the semantic information of the visited venues to model user behaviour patterns; cluster the users with similar patterns into the same community. They assume that users’ check-ins specify the exact venues they visited. However, it has been observed that more than half of users’ check-ins, e.g., in Twitter and Foursquare , are venueless ( i.e., exact venues visited are unknown). Motivated by this observation, this work as the first attempt investigates the hidden community detection by exploring all check-ins ( i.e., check-ins with/without exact venues). The idea is to identify a list of nearby venues around venueless check-ins. To obtain unfailing regularity even though it is uncertain on which venues have been eventually visited, we develop methods based on the probabilistic venue sequences. By tackling a number of unique technical challenges, the rich and comprehensive information disclosed from all check-ins allows us to uncover communities with high modularity which cannot be achieved by state-of-the-art systems. Empirical study conducted on both real-world and synthetic datasets show the efficiency and effectiveness of our methods. [ABSTRACT FROM AUTHOR]

WW International's newly acquired telehealth unit—which specializes in helping patients get prescription weight management drugs like Ozempic—could serve as an "on-ramp" to the company's traditional weight loss programs, Goldman Sachs says. [ABSTRACT FROM AUTHOR]

Let 1 ≤ p < 2 . In this paper, we show that there always exist arrays of rowwise independent random variables { X n k , 1 ≤ k ≤ n , n ≥ 1 } with the same distribution as X , such that n − 1 / p ∑ k = 1 n X n k → 0 a.s . holds if and only if E X = 0 and E | X | β < ∞ for any β ∈ ( p , 2 p ) . This says that the moment gap of the necessary and sufficient condition for the strong law of large numbers between the sequence ( β = p ) and the array ( β = 2 p ) is fulfilled. Analogous results are also obtained for the law of the single logarithm. [ABSTRACT FROM AUTHOR]

Highlights: [•] We study the border collision bifurcations in a piecewise linear map with three slopes depending upon real parameters. [•] We analytically calculate the autocorrelation sequence and the power spectral density for specific values of parameters. [•] We show a relation between border collision bifurcation and the type of bandwidth of chaotic signals. [•] Our study can permit to generate chaotic signals with a specific bandwidth. [ABSTRACT FROM AUTHOR]

Scientists decode the genomic sequence of 700,000-year-old horse. Researchers reveal subepicardial endothelial cells working as major source for coronary arteries in embryonic ventricle wall. Chinese 'container hospitals' ready to deploy in Africa. Asian Cancer Research Group and BGI report new evidence for the genetic bases of liver cancer. Taiwan technology lifts biomedical collaboration. SIIC to provide funding to biotech projects. [ABSTRACT FROM AUTHOR]

Information concerning DNA and amino acid sequences is increasing explosively year by year with development of analytical methods using whole genome sequences from lots of species including human. As for patent applications, enormous sequence data are stored into patent database, and it would be necessary to establish effective way to store the sequence data and well-utilization for sequence search. In this paper, four issues concerning sequences in patent applications were picked up and discussed, 1. Registration of short sequences, 2. Registration of long sequences and huge number of sequence, 3. Redundant sequence database, 4. Sequence data submission standards (XML). Finally, credibility of sequence data in patent applications is also discussed. [ABSTRACT FROM AUTHOR]

Abstract: Several models in data analysis are estimated by minimizing the objective function defined as the residual sum of squares between the model and the data. A necessary and sufficient condition for the existence of a least squares estimator is that the objective function attains its infimum at a unique point. It is shown that the objective function for Parafac-2 need not attain its infimum, and that of DEDICOM, constrained Parafac-2, and, under a weak assumption, SCA and Dynamals do attain their infimum. Furthermore, the sequence of parameter vectors, generated by an alternating least squares algorithm, converges if it decreases the objective function to its infimum which is attained at one or finitely many points. [Copyright &y& Elsevier]

Lists several dance schools in Great Britain that offer any form of ballroom/Latin or social dance forms. Dallas Dance Club; Academy of Ballroom Dancing; Chelmsford Dance Centre; Kings Palais of Dance.

Presents information on several dance schools and events in Great Britain in 2005. Athene School of Dancing in East Anglia, England; Charity Dance in East Anglia; PB Dance Studio in Manchester.